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Surrogate-assisted multi-objective particle swarm optimization for the operation of CO2 capture using VPSA

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  • Alkebsi, Khalil
  • Du, Wenli

Abstract

Multi-objective evolutionary algorithms (MOEAs) have received increasing attention over the past few decades. However, when applying MOEAs to solve computationally expensive real-world applications, they are often criticized due to the large number of function evaluations required. To this end, we proposed a method, called MOPSONN-EGO, for dealing with expensive multi-objective problems. MOPSONN-EGO uses Kriging models to approximate the objective function of expensive problems. The recently proposed MOPSONN algorithm is used to search the landscape of the Kriging models. The cheap-to-evaluate expected improvement matrix is adopted to select infill samples to update the Kriging models. Empirical results on several benchmark problems show the competitive performance of MOPSONN-EGO. Moreover, MOPSONN-EGO is applied to solve the multi-objective optimization problem of the Vacuum pressure swing adsorption (VPSA) for carbon dioxide capture. The simulation results are validated against the expensive model using normalized root mean square error (NRMSE) and compared to the One-shot surrogate modeling strategy. The results reveal the significant efficiency of the proposed algorithm in approximating the real Pareto front of VPSA using only a limited number of detailed model evaluations. Moreover, the obtained best values for each objective with their corresponding decision variables values are reported.

Suggested Citation

  • Alkebsi, Khalil & Du, Wenli, 2021. "Surrogate-assisted multi-objective particle swarm optimization for the operation of CO2 capture using VPSA," Energy, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221003273
    DOI: 10.1016/j.energy.2021.120078
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    References listed on IDEAS

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    1. Haoxiang Jie & Yizhong Wu & Jianjun Zhao & Jianwan Ding & Liangliang, 2017. "An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems," Journal of Global Optimization, Springer, vol. 67(1), pages 399-423, January.
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    Cited by:

    1. Carine M. Rebello & Márcio A. F. Martins & Daniel D. Santana & Alírio E. Rodrigues & José M. Loureiro & Ana M. Ribeiro & Idelfonso B. R. Nogueira, 2021. "From a Pareto Front to Pareto Regions: A Novel Standpoint for Multiobjective Optimization," Mathematics, MDPI, vol. 9(24), pages 1-21, December.

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